Principal Engineer - Enterprise Scalability

At Klaviyo, we help businesses build stronger relationships with their customers. As our customers grow, our platform needs to support greater data volumes, more demanding workloads, and increasingly complex operations—with the performance and reliability they depend on.

Engineering at this scale takes technical depth, sound judgment, and close collaboration. You’ll work across teams to solve challenging problems, turn architectural improvements into production results, and help build a platform ready for our largest customers.

Your Impact

We are seeking a senior technical leader to own Klaviyo’s approach to enterprise scalability, performance, reliability, and multi-region readiness. Reporting to a VP of Engineering, you will be a senior individual contributor with no direct reports. You will lead through technical expertise, hands-on engineering, and influence, partnering across teams to deliver improvements that matter to our customers.

You will identify and remove major bottlenecks, drive platform-wide architectural changes, and establish clear measures of readiness for our largest tenants and most demanding workloads. Your work will span system design, profiling, implementation, and production rollout. You will also help teams adopt practical tools and repeatable patterns that make performance and reliability part of everyday engineering.

This role suits an experienced engineer who can move between deep technical investigation and clear executive communication, make and defend architectural decisions, and build alignment across teams.

Reports to: VP of Engineering
Direct reports: None

Location: Remote (Only applicable to candidates outside of MA. Requires travel to Boston HQ at least once per quarter)

What You’ll Do

• Define what scale readiness means. Establish measurable targets for latency, throughput, error rates, and capacity. Build a shared scorecard and align teams on service-level objectives, error budgets, and performance expectations for critical enterprise workloads.

• Remove the bottlenecks that matter most. Use production data, profiling, and reproducible benchmarks to identify constraints. Partner with service owners to implement fixes and verify improvements under representative workloads.

• Drive architectural change. Design and implement sharding, partitioning, caching, back-pressure, and workload isolation strategies. Advance multi-region readiness and build safe paths for high-volume data migrations.

• Make scale engineering repeatable. Create lightweight benchmarks, profiling harnesses, and test environments that teams can use independently. Pair with engineers to turn successful fixes into reusable patterns.

• Build readiness into delivery. Lead scalability reviews and establish clear, proportionate release gates that surface risks early and help teams ship confidently.

• Turn incidents into lasting improvements. Lead technical deep dives, identify systemic causes, and work with teams to deliver and validate fixes that reduce recurring incidents.

• Apply AI where it improves outcomes. Use AI-assisted profiling, workload modeling, synthetic traffic, anomaly detection, and guided runbooks to improve investigation and response. Establish explicit guardrails, evaluate results, and measure operational impact.

• Connect engineering decisions to customer needs. Communicate priorities, tradeoffs, and results clearly to engineers and executives, tying technical investments to customer experience, enterprise growth, and operating efficiency.

What You’ll Bring

• Deep experience scaling multi-tenant SaaS. You bring 12+ years of relevant engineering experience, with a track record of removing significant bottlenecks and demonstrating measurable production impact.

• Strong performance and distributed systems expertise. You have hands-on experience with performance engineering, capacity planning, sharding and partitioning, caching, back-pressure, tenant isolation, multi-region architectures, and large-scale migrations.

• Engineering depth from diagnosis through delivery. You can investigate unfamiliar systems, test hypotheses, write production code, and work with service owners to roll out changes safely.

• Sound technical judgment. You use evidence to weigh architectural tradeoffs and know when to address an immediate constraint versus invest in a broader platform change.

• Leadership across organizational boundaries. You align teams around shared outcomes, clarify ownership, and build practical standards that help engineers move faster.

• Clear communication. You explain complex technical decisions and their business implications to both engineering and executive audiences.

• Practical AI fluency. You have applied AI tools or automation to engineering work and can evaluate their usefulness, limitations, and operational risks. You are curious about new approaches and disciplined about validating them.

What Success Looks Like in 6–12 Months

• A company-wide scale scorecard is adopted and reviewed regularly, with agreed targets and clear ownership for critical enterprise workloads.

• Two to three high-impact bottlenecks are removed, with reproducible benchmarks demonstrating sustained improvements.

• Our largest customer workloads show measurable improvements in tail latency, throughput, and error rates.

• Multi-region readiness and high-volume migration plans have clear priorities, tested assumptions, and actionable next steps.

• Teams use shared benchmarks and readiness checks to catch regressions earlier, and repeat P0 incidents decline.

• AI-assisted tools demonstrate measurable value in investigation or incident response, such as faster regression isolation or fewer noisy alerts, with appropriate controls and observability.

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